• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >Component Ratio-Based Distances for Cross-Source PolSAR Image Classification
【24h】

Component Ratio-Based Distances for Cross-Source PolSAR Image Classification

机译：基于组分比的跨源POLSAR图像分类的距离

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Many polarimetric features, including decomposition components, can be extracted from polarimetric synthetic aperture radar (PolSAR) data. The polarimetric features usually reflect the physical mechanisms of ground targets and play an important role in PolSAR image classification. However, the feature values may vary largely due to the differences in system parameters of PolSAR sensors, which result in that the trained classifiers on sample data from one source PolSAR image scene may perform poorly in another source PolSAR image scene. The direct use of polarimetric features can produce wrong identifications. In this letter, we mainly deal with the components extracted by different decomposition methods and proposed a simple but efficient component ratio-based distance (CRD), which is an intracross-component distance, in contrast with component-to-component distances. The combinations with $mathcal {L}_{1}$ distance and $chi <^>{2}$ distance can generate $mathcal {L}_{1}$ -CRD and $chi <^>{2}$ -CRD and benefit from their robustness to small values. CRDs capture correlations between scattering components with only a linear computational complexity. Finally, we replace the distance measurement in k-nearest neighbor (KNN) with CRDs and employ the improved classifiers to classify PolSAR images. Based on the ratios of scattering components, CRD can also be used for cross-source PolSAR images, ignoring the differences in sensors, acquired time, imaging scenes, and even wavebands. Preliminary experiments on real PolSAR data sets demonstrate promising results of CRDs for image classification.
机译：可以从偏振合成孔径雷达（POLSAR）数据中提取许多偏振特征，包括分解组件。偏振特征通常反映地面目标的物理机制，并在POLSAR图像分类中发挥重要作用。然而，特征值可能很大程度上是由于波斯卡传感器的系统参数的差异，这导致从一个源POLSAR图像场景上的样本数据上的训练分类器可以在另一个源极的波斯卡图像场景中执行不良。直接使用偏振功能可以产生错误的识别。在这封信中，我们主要处理由不同分解方法提取的组件，并提出了一种简单但有效的组分比基于距离（CRD），其是颅内分量距离，与组件到组件距离相比。使用$ mathcal {l} _ {1} $距离和$ chi <^> {2} $距离可以生成$ mathcal {l} _ {1} $ -crd和$ chi <^> { 2} $ -crd并从其稳健性到小值中受益。 CRD仅具有线性计算复杂度的散射组件之间的相关性。最后，我们用CRD替换K-Collect邻（KNN）中的距离测量，并采用改进的分类器来分类POLSAR图像。基于散射组件的比率，CRD也可以用于跨源POLSAR图像，忽略传感器中的差异，获取的时间，成像场景和偶数波段。真正的Polsar数据集的初步实验表明了用于图像分类的CRD的有希望的结果。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号